PMLE practice questions 231–240

1. Question 231 · 364 in bank

   You have a demand forecasting pipeline in production that uses Dataflow to preprocess raw data prior to model training and prediction. During preprocessing, you employ Z-score normalization on data stored in BigQuery and write it back to BigQuery. New training data is added every week. You want to make the process more efficient by minimizing computation time and manual intervention. What should you do?

   • ANormalize the data using Google Kubernetes Engine.
   • BTranslate the normalization algorithm into SQL for use with BigQuery.
   • CUse the normalizer_fn argument in TensorFlow's Feature Column API.
   • DNormalize the data with Apache Spark using the Dataproc connector for BigQuery.

   Show answer

   Answer: B

   Explanation

   B. I think. BiqQuery definitely minimizes computational time for normalization. I think it would also minimize manual intervention. For data normalization in dataflow you'd have to pass in values of mean and standard deviation as a side-input. That seems more work than a simple SQL query

   Source: Adapted from community discussion — maartenalexander

   References

   • Google Cloud documentation

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2. Question 232 · 364 in bank

   You need to design a customized deep neural network in Keras that will predict customer purchases based on their purchase history. You want to explore model performance using multiple model architectures, store training data, and be able to compare the evaluation metrics in the same dashboard. What should you do?

   • ACreate multiple models using AutoML Tables.
   • BAutomate multiple training runs using Cloud Composer.
   • CRun multiple training jobs on AI Platform with similar job names.
   • DCreate an experiment in Kubeflow Pipelines to organize multiple runs.

   Show answer

   Answer: D

   Explanation

   The best approach is to create an experiment in Kubeflow Pipelines to organize multiple runs. Option A is incorrect because AutoML Tables is a managed machine learning service that automates the process of building machine learning models from tabular data. It does not provide the flexibility to customize the model architecture or explore multiple model architectures. Option B is incorrect because Cloud Composer is a managed workflow orchestration service that can be used to automate machine learning workflows. However, it does not provide the same level of flexibility or scalability as Kubeflow Pipelines. Option C is incorrect because running multiple training jobs on AI Platform with similar job names will not allow you to easily organize and compare the results.

   Source: Adapted from community discussion — tavva_prudhvi

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3. Question 233 · 364 in bank

   You are developing a Kubeflow pipeline on Google Kubernetes Engine. The first step in the pipeline is to issue a query against BigQuery. You plan to use the results of that query as the input to the next step in your pipeline. You want to achieve this in the easiest way possible. What should you do?

   • AUse the BigQuery console to execute your query, and then save the query results into a new BigQuery table.
   • BWrite a Python script that uses the BigQuery API to execute queries against BigQuery. Execute this script as the first step in your Kubeflow pipeline.
   • CUse the Kubeflow Pipelines domain-specific language to create a custom component that uses the Python BigQuery client library to execute queries.
   • DLocate the Kubeflow Pipelines repository on GitHub. Find the BigQuery Query Component, copy that component's URL, and use it to load the component into your pipeline. Use the component to execute queries against BigQuery.

   Show answer

   Answer: D

   Explanation

   D. Kubeflow pipelines have different types of components, ranging from low- to high-level. They have a ComponentStore that allows you to access prebuilt functionality from GitHub.

   Source: Adapted from community discussion — maartenalexander

   References

   • Google Cloud documentation

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4. Question 234 · 364 in bank

   You are building a model to predict daily temperatures. You split the data randomly and then transformed the training and test datasets. Temperature data for model training is uploaded hourly. During testing, your model performed with 97% accuracy; however, after deploying to production, the model's accuracy dropped to 66%. How can you make your production model more accurate?

   • ANormalize the data for the training, and test datasets as two separate steps.
   • BSplit the training and test data based on time rather than a random split to avoid leakage.
   • CAdd more data to your test set to ensure that you have a fair distribution and sample for testing.
   • DApply data transformations before splitting, and cross-validate to make sure that the transformations are applied to both the training and test sets.

   Show answer

   Answer: B

   Explanation

   B. If you do time series prediction, you can't borrow information from the future to predict the future. If you do, you are artificially increasing your accuracy.

   Source: Adapted from community discussion — maartenalexander

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5. Question 235 · 364 in bank

   You are developing models to classify customer support emails. You created models with TensorFlow Estimators using small datasets on your on-premises system, but you now need to train the models using large datasets to ensure high performance. You will port your models to Google Cloud and want to minimize code refactoring and infrastructure overhead for easier migration from on-prem to cloud. What should you do?

   • AUse AI Platform for distributed training.
   • BCreate a cluster on Dataproc for training.
   • CCreate a Managed Instance Group with autoscaling.
   • DUse Kubeflow Pipelines to train on a Google Kubernetes Engine cluster.

   Show answer

   Answer: A

   Explanation

   A. AI platform provides lower infrastructure overhead and allows you to not have to refactor your code too much (no containerization and such, like in KubeFlow).

   Source: Adapted from community discussion — maartenalexander

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6. Question 236 · 364 in bank

   You have trained a text classification model in TensorFlow using AI Platform. You want to use the trained model for batch predictions on text data stored in BigQuery while minimizing computational overhead. What should you do?

   • AExport the model to BigQuery ML.
   • BDeploy and version the model on AI Platform.
   • CUse Dataflow with the SavedModel to read the data from BigQuery.
   • DSubmit a batch prediction job on AI Platform that points to the model location in Cloud Storage.

   Show answer

   Answer: A

   Explanation

   There are some drawbacks to option D. Cost: Submitting a batch prediction job on AI Platform is a paid service. The cost will depend on the size of the model and the amount of data that you are predicting. Complexity: Submitting a batch prediction job on AI Platform requires you to write some code. This can be a challenge if you are not familiar with AI Platform. Performance: Submitting a batch prediction job on AI Platform may not be as efficient as using BigQuery ML. This is because AI Platform needs to load the model into memory before it can run the predictions. Overall, option D is a viable option, but it may not be the best option for all situations.

   Source: Adapted from community discussion — Darshan12

   References

   • Google Cloud documentation
   • Google Cloud documentation
   • Google Cloud documentation
   • Google Cloud documentation

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7. Question 237 · 364 in bank

   You work with a data engineering team that has developed a pipeline to clean your dataset and save it in a Cloud Storage bucket. You have created an ML model and want to use the data to refresh your model as soon as new data is available. As part of your CI/CD workflow, you want to automatically run a Kubeflow Pipelines training job on Google Kubernetes Engine (GKE). How should you architect this workflow?

   • AConfigure your pipeline with Dataflow, which saves the files in Cloud Storage. After the file is saved, start the training job on a GKE cluster.
   • BUse App Engine to create a lightweight python client that continuously polls Cloud Storage for new files. As soon as a file arrives, initiate the training job.
   • CConfigure a Cloud Storage trigger to send a message to a Pub/Sub topic when a new file is available in a storage bucket. Use a Pub/Sub-triggered Cloud Function to start the training job on a GKE cluster.
   • DUse Cloud Scheduler to schedule jobs at a regular interval. For the first step of the job, check the timestamp of objects in your Cloud Storage bucket. If there are no new files since the last run, abort the job.

   Show answer

   Answer: C

   References

   • Google Cloud documentation

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8. Question 238 · 364 in bank

   You want to rebuild your ML pipeline for structured data on Google Cloud. You are using PySpark to conduct data transformations at scale, but your pipelines are taking over 12 hours to run. To speed up development and pipeline run time, you want to use a serverless tool and SQL syntax. You have already moved your raw data into Cloud Storage. How should you build the pipeline on Google Cloud while meeting the speed and processing requirements?

   • AUse Data Fusion's GUI to build the transformation pipelines, and then write the data into BigQuery.
   • BConvert your PySpark into SparkSQL queries to transform the data, and then run your pipeline on Dataproc to write the data into BigQuery.
   • CIngest your data into Cloud SQL, convert your PySpark commands into SQL queries to transform the data, and then use federated queries from BigQuery for machine learning.
   • DIngest your data into BigQuery using BigQuery Load, convert your PySpark commands into BigQuery SQL queries to transform the data, and then write the transformations to a new table.

   Show answer

   Answer: D

   Explanation

   ANS: A https://cloud.google.com/data-fusion#section-1 - Data Fusion is a serverless approach leveraging the scalability and reliability of Google services like Dataproc means Data Fusion offers the best of data integration capabilities with a lower total cost of ownership. - BigQuery is serverless and supports SQL. - Dataproc is not serverless, you have to manage clusters. - Cloud SQL is not serverless, you have to manage instances.

   Source: Adapted from community discussion — Celia20210714

   References

   • Google Cloud documentation
   • Google Cloud documentation

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9. Question 239 · 364 in bank

   Select 2

   You have a functioning end-to-end ML pipeline that involves tuning the hyperparameters of your ML model using AI Platform, and then using the best-tuned parameters for training. Hypertuning is taking longer than expected and is delaying the downstream processes. You want to speed up the tuning job without significantly compromising its effectiveness. Which actions should you take? (Choose two.)

   • ADecrease the number of parallel trials.
   • BDecrease the range of floating-point values.
   • CSet the early stopping parameter to TRUE.
   • DChange the search algorithm from Bayesian search to random search.
   • EDecrease the maximum number of trials during subsequent training phases.

   Show answer

   Answer: CE

   Explanation

   Answer: B & C (Ref: https://cloud.google.com/ai-platform/training/docs/using-hyperparameter-tuning) (A) Decreasing the number of parallel trials will increase tuning time. (D) Bayesian search works better and faster than random search since it's selective in points to evaluate and uses knowledge of previouls evaluated points. (E) maxTrials should be larger than 10*the number of hyperparameters used. And spanning the whole minimum space (10*num_hyperparams) already takes some time. So, lowering maxTrials has little effect on reducing tuning time.

   Source: Adapted from community discussion — Paul_Dirac

   References

   • Google Cloud documentation
   • Google Cloud documentation

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10. Question 240 · 364 in bank

    Your team is building an application for a global bank that will be used by millions of customers. You built a forecasting model that predicts customers' account balances 3 days in the future. Your team will use the results in a new feature that will notify users when their account balance is likely to drop below $25. How should you serve your predictions?

    • A1. Create a Pub/Sub topic for each user. 2. Deploy a Cloud Function that sends a notification when your model predicts that a user's account balance will drop below the $25 threshold.
    • B1. Create a Pub/Sub topic for each user. 2. Deploy an application on the App Engine standard environment that sends a notification when your model predicts that a user's account balance will drop below the $25 threshold.
    • C1. Build a notification system on Firebase. 2. Register each user with a user ID on the Firebase Cloud Messaging server, which sends a notification when the average of all account balance predictions drops below the $25 threshold.
    • D1. Build a notification system on Firebase. 2. Register each user with a user ID on the Firebase Cloud Messaging server, which sends a notification when your model predicts that a user's account balance will drop below the $25 threshold.

    Show answer

    Answer: D

    Explanation

    D is correct. Firebase is designed for exactly this sort of scenario. Also, it would not be possible to create millions of pubsub topics due to GCP quotas https://cloud.google.com/pubsub/quotas#quotas https://firebase.google.com/docs/cloud-messaging

    Source: Adapted from community discussion — SlipperySlope

    References

    • Google Cloud documentation

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PMLE practice, question 231. 364 questions available.